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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationWed, 04 Nov 2009 09:22:44 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/04/t1257351999kuec2zsa4pthdtm.htm/, Retrieved Fri, 31 Oct 2025 19:28:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53693, Retrieved Fri, 31 Oct 2025 19:28:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact374

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [Ws 5 bivariate X ...] [2009-11-04 15:39:55] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-    D    [Bivariate Explorative Data Analysis] [Ws 5 bivariate X ...] [2009-11-04 16:22:44] [ba02bcb7e07025bbb7f8a074d38ad767] [Current]
-    D      [Bivariate Explorative Data Analysis] [WS 5 bivariate EDA] [2009-11-22 17:49:18] [005293453b571dbccb80b45226e44173]
-    D        [Bivariate Explorative Data Analysis] [WS 5 Bivariate EDA 1] [2009-11-22 17:53:15] [005293453b571dbccb80b45226e44173]
-    D          [Bivariate Explorative Data Analysis] [WS 5 Bivariate EDA 2] [2009-11-22 18:01:47] [005293453b571dbccb80b45226e44173]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
94.3
99.4
115.7
116.8
99.8
96.0
115.9
109.1
117.3
109.8
112.8
110.7
100.0
113.3
122.4
112.5
104.2
92.5
117.2
109.3
106.1
118.8
105.3
106.0
102.0
112.9
116.5
114.8
100.5
85.4
86.6
109.9
100.7
115.5
100.7
99.0
102.3
108.8
105.9
113.2
95.7
80.9
113.9
98.1
102.8
104.7
95.9
94.6
101.6
103.9
110.3
114.1
96.8
87.4
111.4
97.4
102.9
112.7
97.0
95.1
96.9
98.6
111.7
109.8
89.9
87.4
104.5
98.1
102.7
105.4
97.0
97.4
Dataseries Y:
Download CSV
Histogram 
16.4
17.8
22.3
22.8
18.3
22.4
23.9
21.3
23.0
21.4
21.2
20.9
17.9
20.7
22.2
19.8
17.7
19.6
20.8
19.8
18.6
21.
18.6
18.9
17.3
20.0
19.9
19.5
16.2
17.6
19.8
19.4
17.2
21.1
17.8
17.5
18.0
19.1
17.7
19.2
15.1
16.3
18.6
17.2
17.8
19.1
16.6
16.0
16.7
17.4
17.9
17.8
13.9
15.9
17.9
15.4
16.4
17.9
15.3
14.6
14.9
15.0
16.7
16.3
11.7
15.1
15.5
15.0
15.4
16.0
14.7
14.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53693&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53693&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53693&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c-0.880995490548963
b0.181809067607487

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.880995490548963 \tabularnewline
b & 0.181809067607487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53693&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.880995490548963[/C][/ROW]
[ROW][C]b[/C][C]0.181809067607487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53693&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53693&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c-0.880995490548963
b0.181809067607487






Descriptive Statistics about e[t]
# observations72
minimum-3.76363968736411
Q1-1.42717009830114
median0.113641698408775
mean1.39741613342576e-18
Q30.84339495695808
maximum5.82732500023022

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -3.76363968736411 \tabularnewline
Q1 & -1.42717009830114 \tabularnewline
median & 0.113641698408775 \tabularnewline
mean & 1.39741613342576e-18 \tabularnewline
Q3 & 0.84339495695808 \tabularnewline
maximum & 5.82732500023022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53693&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-3.76363968736411[/C][/ROW]
[ROW][C]Q1[/C][C]-1.42717009830114[/C][/ROW]
[ROW][C]median[/C][C]0.113641698408775[/C][/ROW]
[ROW][C]mean[/C][C]1.39741613342576e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.84339495695808[/C][/ROW]
[ROW][C]maximum[/C][C]5.82732500023022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53693&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53693&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations72
minimum-3.76363968736411
Q1-1.42717009830114
median0.113641698408775
mean1.39741613342576e-18
Q30.84339495695808
maximum5.82732500023022


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
y <- as.ts(y)
mylm <- lm(y~x)
cbind(mylm$resid)
library(lattice)
bitmap(file='pic1.png')
plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1a.png')
plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1b.png')
plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]')
grid()
dev.off()
bitmap(file='pic1c.png')
plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(mylm$resid,main='Histogram of e[t]')
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t]   bw = ',par1),bw=par1)
} else {
densityplot(~mylm$resid,col='black',main='Density Plot of e[t]')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(mylm$resid,main='QQ plot of e[t]')
qqline(mylm$resid)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='pic5.png')
acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'c',1,TRUE)
a<-table.element(a,mylm$coeff[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'b',1,TRUE)
a<-table.element(a,mylm$coeff[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(mylm$resid))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')